ESHLOS guidance for neuro-adaptive path following control of underactuated surface vessels

Xia, Guoqing; Wang, Xinwei; Zhao, Bo; Han, Zhiwei; Ren, Zheda

doi:10.1016/j.oceaneng.2022.112894

Cited by 4 publications

(5 citation statements)

References 27 publications

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“…The obstacle-avoidance path consists of 5 waypoints {P 1 (0, 0), P 2 (50, Based on Figure 1, the simulation scenario for tracking the path of the USV and avoiding obstacles is designed as depicted in Figure 3. The initial position of the USV is set to To demonstrate the superiority of the guidance and control scheme proposed in this paper (denoted as "A"), the control scheme based on ESHLOS guidance law proposed in [10] (denoted as "B") is compared and simulated. The simulation results are shown in Figures 4-9.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Theorem 1. For the USV model ( 1) and the lateral error model ( 4), under assumptions 1, 2, and 3, the heading guidance law (18) and the system control laws (31) and (38) are applied to the USV control system based on the designed performance function (10), the following conclusions are obtained:…”

Section: Stability Analysismentioning

confidence: 99%

“…To demonstrate the superiority of the guidance and control scheme proposed in this paper (denoted as "A"), the control scheme based on ESHLOS guidance law proposed in [10] (denoted as "B") is compared and simulated. The simulation results are shown in Figures 4-9.…”

Section: Casementioning

confidence: 99%

“…Straight-line path-following was achieved by defining virtual target points along the desired path and designing corresponding algorithms for both their motion and that of the USV. Owing to the influence of environmental factors, such as wind, waves, and currents, disturbance observers [7,8] and neural networks [9,10] have been used to improve the anti-interference performance of the control system in most studies. Additionally, the authors of [11][12][13] further considered input-saturation issues.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Path-Following and Obstacle-Avoidance Control of USV Based on Finite-Distance Convergence

Wei,

Zhang,

Liu

et al. 2023

JMSE

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The control problem of avoidance-path-following is a critical consideration in the research of unmanned surface vehicle (USV) navigation control, and it holds great significance for the navigation safety of USVs. A guidance and control scheme based on finite-distance convergence is proposed in this paper. First, the requirements for the USV to avoid obstacles from the perspective of path-following lateral error are analyzed. Then, a new performance function with finite-distance convergence is proposed to constrain the lateral error. Based on this, a heading guidance law and a backstepping controller are designed to ensure that the lateral error converges to a steady-state value within the prescribed navigation distance and that the stability is maintained, satisfying the requirements of obstacle avoidance for the USV. In addition, an adaptive velocity command is designed to adjust the velocity with the lateral error, which, to a certain extent, avoids the saturation of the heading actuator caused by the large lateral error. Finally, it is proven through theory and simulation that the control algorithm can guide the USV to achieve avoidance-path-following within a limited distance and to avoid obstacles effectively.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Stability Analysismentioning

confidence: 99%

Section: Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Path-Following and Obstacle-Avoidance Control of USV Based on Finite-Distance Convergence

Wei,

Zhang,

Liu

et al. 2023

JMSE

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show abstract

“…On the other hand, the path following algorithm attempts to trace a predefined geometric path instead of following a time-related path (Peng et al, 2023;Murali et al, 2022). Since the path following algorithm does not require time constraints, it is widely preferred in the motion control of USVs (Yu et al, 2023;Gonzalez et al, 2022;Xia et al, 2022;Yang et al, 2022). Path following algorithm consists of three parts: navigation, guidance law and control (Keat et al, 2022;Hung et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Intelligent vector-based path following guidance law for unmanned surface vehicles

ÜNAL¹,

Akkaş²,

Atali³

2023

Preprint

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This study proposes a significant improvement on the classical vector field guidance law. Classical vector field method results in imaginary number some cases. In numerical calculation, imaginary part of the number ignores to obtain well-posed solution. However, negligence of imaginary part negatively affects the optimization workflow and it leads to larger cross tracking errors. In this study, classical vector field method was modified to obtain real numbers in all cases. Owing to the modified new method, cross tracking errors were diminished by 10% compared to the traditional vector field method. Second contribution of this study is to integrate the surrogate optimization technique to the developed robust vector field method in order to tune parameters of the guidance law. To the best of our knowledge, we are the first to combine surrogate optimization method with vector field path following algorithm. Vector field path following algorithm requires optimizing four important key parameters. While the unmanned surface vehicle performs the multi-command tasks, determining the optimum values of these four key parameters for each mission increases the computational costs significantly. Since the surrogate optimization method is more suitable for the time-consuming objective functions, it is preferred instead of the most popular optimization technique, genetic algorithm. Surrogate optimization method integrated robust vector field path following algorithm determines optimum parameters approximately 10 times faster than the genetic algorithm integrated vector field method. In addition to the time advantage of the developed model, the proposed method provides a navigation that causes less cross tracking errors compared to classical technique.

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Adaptive neural dynamic surface composite control for path following of underactuated vessels with fixed-time convergence

Liu,

Li,

Wang

2023

Ocean Engineering

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ESHLOS guidance for neuro-adaptive path following control of underactuated surface vessels

Cited by 4 publications

References 27 publications

Path-Following and Obstacle-Avoidance Control of USV Based on Finite-Distance Convergence

Path-Following and Obstacle-Avoidance Control of USV Based on Finite-Distance Convergence

Intelligent vector-based path following guidance law for unmanned surface vehicles

Adaptive neural dynamic surface composite control for path following of underactuated vessels with fixed-time convergence

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